Automated technique for coronary artery disease characterization and classification using DD-DTDWT in ultrasound images

Abstract Heart is one of the important as well as hardest working organ of human body. Cardiac ischemia is the condition where sufficient blood and oxygen will not reach the heart muscle due to narrowed arteries of the heart. This condition is called coronary artery disease. Several non-invasive diagnostic tests fail to reveal exact impact of coronary artery disease on myocardial segments. The ultrasound images can explore major impact on ventricular muscle segments due to ischemia and complication of acute coronary syndrome. Computer aided diagnosis tools can predict coronary artery disease in its early stage so that patients can undergo treatment and save their life. This paper presents a novel computer aided diagnosis system for the automated detection of coronary artery disease using echocardiography images taken from four chamber heart. Proposed method uses double density-dual tree discrete wavelet transform (DD-DTDWT) to decompose the images into different frequency sub-bands. Then various entropy features are extracted from these sub-bands. The obtained dimension of the features is reduced using marginal fisher analysis (MFA) and optimal features are selected using feature ranking methods. The proposed method achieved promising accuracy of 96.05%, sensitivity of 96.12%, and specificity of 96.00% for linear discriminant classifier using entropy ranking method with twelve features. We have also proposed coronary artery disease risk index (CADRI) to categorize diseased subjects from normal subjects using a single value. Thus, it can be used as a diagnosis tool in hospitals and polyclinics for confirming the findings of clinicians.